tbuckley/cabot-search huggingface dataset:
Supporting high-risk oncology patients with an outpatient transitional care management program.
Authors: Manisha Bhattacharya, Regina Graziano, Evanne Wahal, Julie Alexander, Lynne O'Connor, Rene Cumberledge, Avigayil East, Dan Paul Zandberg, Roby Antony Thomas
Journal: Journal of Clinical Oncology (2022)

313 Background: Transitional Care Management initiatives (TCM) financially support comprehensive outpatient followup in the first 30 days after acute care discharge. TCM programs require patient contact within 2 days of discharge, an outpatient appointment within 7-14 days, patient education, and moderate-high complexity medical decision-making that supports successful outpatient transition. To date, TCM is often utilized to provide additional reimbursement for qualifying primary care post-discharge followup, but could be applied to align incentives in busy oncology practices. We present a novel application of TCM to enhance post-discharge cancer care with the goal of reducing readmissions for high-risk oncology patients. Methods: We identified patients discharged from our health system’s cancer hospital with a “Highest” risk of readmission as determined by an internally developed machine learning clinical algorithm built into the EMR. Patients were targeted with a post-discharge phone call within 48 hours offering an in-person or telemedicine appointment within 72 hours. The Transitional Care Clinic (TCC) visit guidelines promote connection to ancillary services such as physical and occupational therapy, medication management, and dietetics. The TCC can offer interventions to address emergent medical needs such as fluids, transfusions, and symptom management. Results: In the first three months of the TCC program, the 30-day readmission risk algorithm identified 136 cancer patients discharged from the designated cancer hospital in the “Highest” readmission risk category. 121 of those patients were previously admitted within one year. 72 of the 136 “Highest” risk patients were referred to the cancer center TCC workflow and 60 patients were eligible for a TCC appointment. 18 patients had appointments scheduled (30%) with 83% completed (15/18). 22% of scheduled appointments were via telemedicine (4/18) and 100% of these appointments were completed. Conclusions: TCM programs can be adapted to reward timely hospital followup for oncology patients after discharge. TCM requirements overlap with several common elements of readmission reduction initiatives, but EMR tools for identifying patients at highest readmission risk may need additional adaptation into clinic workflows to promote a high capture rate that justifies program support. Future longitudinal cohort analysis will establish if the TCC is able to reduce cancer-related readmissions in high-risk populations.
Quality of care of Asian and White patients with lung cancer: Single-institution study.
Authors: Xiao Hu, John Melson, Stacey Pan, Yana Salei, Yu Cao
Journal: Journal of Clinical Oncology (2022)

115 Background: Cancer is the leading cause of death in Asian Americans (AA), the fastest growing racial/ethnic group in the US. Lung cancer is a leading cancer diagnosis and cause of cancer death in AA. Yet data on the quality of lung cancer care in AA are limited. This retrospective study examines racial disparities in lung cancer care at an urban academic medical center serving a large proportion of Asian patients. Methods: Newly diagnosed patients with lung cancer from 01/01/2014 to 12/31/2019 were identified in the Tufts Medical Center cancer registry; clinical data were collected through 05/31/2022. Patient demographics, smoking status, utilization of screening low dose CT (LDCT), disease characteristics (diagnosis stage, histology, driver mutation presence), and treatment history were compared between Asian and White patients. The influence of race on presenting stage was evaluated via ordinal logistic regression. Time to treatment initiation (TTI) and overall survival (OS) in Asian and White patients were analyzed via log-rank tests. Multivariable Cox regression adjusting for baseline patient characteristics, tumor histology and stage was performed to evaluate the impact of race on OS. Results: Of 145 Asian and 476 White patients, the Asian cohort had significantly (p &lt; 0.001) older age (72 vs 68 years), more male representation (74.5% vs 43.9%) and never-smokers (31.0% vs 9.7%). Of 45 Asian never-smokers, women comprised 75.6%. Of 216 patients eligible for lung cancer screening by the 2013 USPSTF criteria, the Asian cohort had relatively lower LDCT utilization (11.9% vs 21.3%, p = 0.198). Asians were 2.13 times (p = 0.003) more likely to be diagnosed with lung cancer at a later stage than White patients, adjusting for age, sex, income, smoking status, and histology. No difference was found in presence of CNS metastasis at diagnosis. Driver mutations were more often found in Asians (45.5% vs 29.4%). Of 206 patients with detected oncogenic mutations, EGFR alterations comprised 62.1% vs 15.0% in Asian and White cohorts, respectively. Asian patients had longer median TTI (1.13 vs 0.83 months, p = 0.005) and more often did not receive cancer directed therapy (12.6% vs 6.1%, p = 0.016). Of 536 patients who received cancer directed therapy, Asians more often received upfront targeted therapy (16.1% vs 2.2%, p &lt; 0.001). No difference was seen in median OS between Asian and White (not reached vs 78.43 months, p = 0.410). Multivariable Cox regression suggested that Asians tended toward better OS (hazard ratio = 0.66, p = 0.091). Conclusions: This study done in Boston shows that Asian patients are diagnosed with lung cancer at more advanced stages and experience longer delays prior to treatment initiation, compared to White patients. Racial disparities persist in lung cancer care, particularly in detection/diagnosis and early management. Yet Asian patients, in aggregate, do not have inferior survival, warranting further research.
Real-world treatment patterns among men with metastatic castration-resistant prostate cancer (mCRPC) in the U.S. Medicare population.
Authors: Stephen J. Freedland, Matthew Davis, Andrew J. Epstein, Bhakti Arondekar, Jasmina I. Ivanova
Journal: Journal of Clinical Oncology (2022)

406 Background: Multiple life-prolonging therapies have been approved for mCRPC, for example novel hormonal therapies (NHT; abiraterone [abi], enzalutamide [enza]), docetaxel (doc), cabazitaxel, sipuleucel-T, and radium-223. This study describes real-world treatment patterns with life-prolonging therapies and sequencing among men with mCRPC in the US Medicare population. Methods: Men newly diagnosed with mCRPC were identified in Medicare fee-for-service claims during 1/1/2014–6/30/2019. Adult men were required to have a diagnosis of prostate cancer, metastasis diagnosis, castration-resistance using a published claims-based algorithm, and continuous insurance coverage for ≥1 year before and ≥6 months after index mCRPC diagnosis unless patients died. Treatment patterns of life-prolonging therapies after mCRPC diagnosis and sequencing were described. Results: Among 14,780 men with mCRPC, median age was 75 years, 10% used NHT in the year prior to mCRPC, and 3% had prior taxane therapy. Median follow-up after mCRPC diagnosis was 17 months. 22% of men received no life-prolonging therapy after mCRPC diagnosis, 78% received ≥1 line of therapy with life-prolonging treatment after mCRPC diagnosis, 42% had ≥2, and 20% had ≥3. The most common first-line (1L) therapies were abi (36%), enza (28%), and doc (16%). The most common second-line (2L) therapies were enza (33%), abi (28%), and doc (15%). The most common third-line (3L) therapies were doc (24%), enza (19%), and abi (17%). Median time from start of 1L to next line of therapy or end of follow-up was 13.7 months, 10.9 months from the start of 2L, and 8.9 months from the start of 3L. The most common 1L to 2L treatment sequences among men with ≥2 lines were NHT followed by a different NHT (33%), chemotherapy followed by NHT (14%), and NHT followed by chemotherapy (13%). There were 5,630 men with ≥2 lines of therapy and ≥1 NHT, of whom 53% had ≥2 NHTs. Conclusions: Substantial proportions of men with mCRPC did not receive a life-prolonging therapy or had only 1L therapy after mCRPC diagnosis, with a 50% fall-off rate after each line of therapy. NHTs were the most common 1L and 2L therapies, and NHT followed by a different NHT was the most common treatment sequence. Further research is needed to understand how treatment patterns change as NHTs and doc are used earlier in the disease continuum and new therapies are introduced and ultimately to identify optimal treatment sequencing.
Implementation of EHR medication-adherence screening tool in breast cancer clinic.
Authors: Beatriz Desanti de Oliveira, David DeStephano, Melissa Parsons Beauchemin, Cynthia Law, Kristina ` Howard, Jason Dennis Wright, Ian Kronish, Dawn L. Hershman, Melissa Kate Accordino
Journal: Journal of Clinical Oncology (2022)

438 Background: Nonadherence to prescribed medications occurs frequently in patient with breast cancer (BC) and can affect BC outcomes as well as outcomes for comorbid conditions. We implemented a process to screen for medication adherence in the electronic health record (EHR) in an urban outpatient BC clinic. Methods: Plan-Do-Study-Act (PDSA) methodology was used to implement a screening process for medication adherence for all patients seen in the outpatient breast oncology clinic. At check-in (via the patient portal or clinic based kiosks), patients were asked to complete an EHR adherence screener. Two PDSA cycles were completed. During cycle one (2/16/22-5/17/22), patients were asked if they received ≥1 prescribed medication; if yes they were asked to complete the questionnaire (y/n); if yes a 3-item questionnaire was used to screen for adherence to all medications over the prior 7 days. Adherence was defined as 3 of 3 responses “none of the time” to “I have missed my medicine;” “I have skipped a dose of my medicine;” and “I did not take a dose of my medicine.” During cycle two (5/17/22-6/5/22) the screener was simplified. Patients were no longer asked to complete the survey; and the survey was modified to 1-item “I did not take a dose of my medicine”, adherence was defined as response of “none of the time”. We evaluated response rate and self-reported non-adherence rate. Results: During PDSA cycle 1 (2/16/22-5/17/22), 2840 visits occurred and 722 (25%) responses were received; 80% noted prescription of ≥1 medication, 38% agreed to complete the survey; and 87% reported adherence to all prescribed medications while 13% reported non-adherence. During PDSA cycle 2 (5/17/22-6/5/22), 512 visits occurred and 172 (33%) responses were received. Of those, 73% reported prescription of ≥1 medication; of those 66%-reported adherence to all prescribed medications, 21% reported non-adherence, and 17% preferred not to answer. Conclusions: This EHR screener is a simple and scalable tool to rapidly screen for medication adherence. Up to a quarter of patients who completed screening reported non-adherence. Further tools are needed to assess adherence among patients who lack access to the patient portal or clinic kiosk, or are uncomfortable checking in with these mechanisms. Future interventions are necessary to further screen potentially non-adherent patients and for interventions to improve adherence once vulnerable patients are identified.
Measures to evaluate quality of care in head and neck cancer: Results of a Delphi study.
Authors: Jesus Garcia-Foncillas, Juan Jesus Cruz Hernandez, Virginia Arrazubi, Yolanda Escobar, Almudena García Castaño, Juan J. Grau, Lara Iglesias, Julio Lambea, Pedro Perez Segura, Cristina Antón, Fernando Caballero, Francisco J. Campos, Maria Bessa, Paula Gratal, Diana Monge, Irene Santamaría Rodríguez, Antonio Rueda
Journal: Journal of Clinical Oncology (2022)

358 Background: Currently, no specific measures are used in Spain to evaluate quality of care in head and neck cancer, and programs such as the Quality Oncology Practice Initiative developed by the American Society of Clinical Oncology does not include head-and-neck-specific measures. Methods: A systematic literature review was carried out to identify measures useful for evaluating quality of care in head and neck cancer. A scientific committee, comprising 9 medical oncologists specialized in head and neck cancer, reviewed the literature findings and developed measures to be evaluated in a 2-step Delphi method. Experts in head and neck cancer practicing in Spain—specialized in medical oncology, radiotherapy oncology, maxillofacial surgery, pathology or otorhinolaryngology—participated in the Delphi, scoring the appropriateness of the measures using a 9-point Likert scale (1, extremely inappropriate; 9, extremely appropriate). Consensus was defined as at least two-thirds of Delphi respondents selecting a score sub-category (1–3, 4–6, or 7–9) that encompassed the median score of the group. Results: Out of the 833 documents found with the literature review, 20 were selected from to identify measures of interest. Fifty measures, covering diagnosis (13), treatment (28), follow-up (5), and outcome (4), were evaluated with the Delphi method. The 52 Delphi participants reached consensus on the appropriateness of using all 50 measures to evaluate the quality of care in head and neck cancer. Measures with lowest scores concerned re-hospitalization shortly after surgery and long hospitalization after surgery. Measures with highest scores regarded the use of imaging for follow-up, a histology study prior to treatment, and complete resection of the tumor. The scientific committee then selected 29 measures based on their applicability and cost-effectiveness and developed index cards with definitions, formulas, acceptable level of attainment, and rationale for their use in clinical practice. Conclusions: The goal of this study was to develop measures to evaluate and improve the quality of care in head and neck cancer. These results show unanimous consensus from a group of experts on the proposed measures for diagnosis, treatment, follow-up, and outcome. The index cards developed with measures are easy to follow and their use could improve quality of care.
Association of patients' primary language, race, and location of care with decision-making for personalized breast cancer risk assessment.
Authors: Angela Rutkowski, Shirlene Paul, Jordan Lieberenz, Lisa Stempel, Rosalinda Alvarado, Mia Alyce Levy
Journal: Journal of Clinical Oncology (2022)

331 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p &lt;.0001). Results shown in Table below. Conclusions: The main variables mediating the decision to opt out of CRA are a non-English primary language, non-White race, and location of care, potentially due to inadequate interpretation services, barriers to education, and staffing shortages. Thus, these women are less likely to receive genetic counseling/testing or supplemental screening. Our study is the first to investigate the factors contributing to the uptake of risk assessment. Further study is needed to determine the clinical impact of this striking disparity.[Table: see text]
Racial and gender disparities in metastatic lung and bronchial cancer (LC) across the United States.
Authors: Devashish Desai, Sachi Singhal, Oladimeji Lanade, Akshita Khosla, Rashmika Potdar
Journal: Journal of Clinical Oncology (2022)

357 Background: LC is one of the leading causes of cancer deaths in the US. Despite advancements in care and screening, disparities continue to be prominent when looking into minority ethnic and racial groups. This study was undertaken to study racial and gender disparity in all-cause mortality (ACM) in patients with LC. Methods: Data was obtained from 2016-18 Nationwide Inpatient Sample (NIS). Cases with a primary diagnosis of LC were identified using ICD-10 CM code “C34”. SAS 9.4 was used for data analysis. Logistic regression was performed to derive the odds ratio (OR). Results: We identified 238,516 cases with LC, 77.4% were Whites, 12.5% African American (AA), 4.7% Hispanics, and 2.8 % Asians and Pacific Islanders (API). As per Table, odds of dying were higher amongst AA, API, and Native Americans (NA) compared to Whites. The odds of ACM for AA and API remain high in all regions compared to Whites. Hispanics have significantly lower odds of ACM compared to Whites in the Southern (S) region, and no statistical difference in other regions. NA have lower odds of dying in the Northeast (NE), no statistical difference in the Midwest (MW), and higher in the S and the West (W). Being female decreases the odds of ACM overall as well as in all the regions when compared to males, but odds of dying are significantly more for females in the W when compared to females in the NE. Males have lower odds of dying in the MW and S regions, and no statistical difference in W when compared to the NE. Conclusions: Despite advancements in treatment modalities and screening, racial minorities remain at higher odds of ACM in all regions. Understanding the complex cause of these disparities and implementing interventions will be key to mitigating these disparities and moving closer to equitable outcomes in LC patients[Table: see text]
Impact of the Cancer and Aging Research Group (CARG) chemotherapy toxicity (tox) risk score on the benefit of a geriatric assessment–driven intervention (GAIN) among older adults with cancer.
Authors: Christiana Crook, Can-Lan Sun, Heeyoung Kim, Enrique Soto Pérez de Celis, Vincent Chung, Marianna Koczywas, Marwan Fakih, Joseph Chao, Leana Cabrera Chien, Kemeberly Charles, Vani Katheria, Monica Trent, Elsa Roberts, Reena Jayani, Jeanine Moreno, Mina S. Sedrak, William Dale, Daneng Li
Journal: Journal of Clinical Oncology (2022)

235 Background: The CARG tox score can predict risk of chemotherapy-related tox in older adults with cancer. GAIN can reduce tox vs standard of care (SOC) among these patients (pts); GAIN’s impact across CARG risk groups is unknown. Methods: A secondary analysis of the GAIN randomized clinical trial (NCT02517034) of pts aged ≥65 (solid tumor diagnosis, starting a new chemotherapy) was performed. Pts were randomized 2:1 to receive GAIN vs SOC and were categorized into low (0-5), medium (6-9), and high (10-20) risk groups according to CARG score. The primary outcome was incidence of grade 3-5 tox. Chi-square/Fisher’s exact tests were used to compare outcomes (GAIN vs SOC, stratified by risk groups). Log-rank tests were used to compare 1-year survival across risk groups. Results: This analysis included 600 pts: 26.5% low risk, 45.2% medium risk, 28.3% high risk. Table shows pt/treatment characteristics. For pts with low/medium risk scores, GAIN demonstrated a 14.0% (95% CI 4.1%-23.9%) reduction in tox vs SOC (p = 0.006). No significant reduction in tox was observed among pts with high risk scores (p = 0.86). One-year survival (GAIN vs SOC) for each risk group was 73.6% vs 67.4% (low risk), 68.5% vs 64.5% (medium risk), and 57.3% vs 61.7% (high risk), respectively (log-rank p = 0.10). Conclusions: Older adults with low/medium, but not high, CARG risk scores benefit from GAIN. Additional strategies may be needed to improve outcomes for pts with high CARG risk scores. Clinical trial information: NCT02517034. [Table: see text]
Racial disparities among patients with multiple myeloma enrolled in clinical trials.
Authors: Nabilah Nishat, Thoasin Bari, Shahidul Islam, Marc Justin Braunstein
Journal: Journal of Clinical Oncology (2022)

89 Background: Tremendous therapeutic progress has been made over the past decade in the field of multiple myeloma (MM). Although the incidence of MM is twice as high for black compared to white individuals, mortality rates remain higher for black patients (Marinac, et al. Blood Ca J 2020). In addition, black Americans are significantly less likely to participate in clinical trials in general (Hong, et al. Am J Prev Med 2021), leading to disproportionate enrollment in studies of novel agents. Various factors may account for these disparities, including access to clinical trials, clinician bias, and hesitancy to enroll in clinical trials among different patient populations. Furthermore, underrepresentation of racial groups within clinical trials impacts the generalizability of important findings from these studies. Here we characterized the racial representation of MM clinical trials. Methods: Randomized clinical trials focused on MM interventions published between 2012-2022 were included. We screened 431 publications during this period and characterized racial demographics as available in the literature. A two-sided Cochran-Armitage Trend Test was used to assess if there was a linear trend in the percentage of black participants in clinical trials over time. Results: Among 431 studies published over the past 10 years, 76 collected over past 5 years that included racial demographic details were included. Among the 38,830 participants, 87.5% were white, 4.8% were black, and 7.7% were other (reported as either “Asian”, “mixed”, or “other”). There was a significant trend toward increased enrollment comparing over time between 2018 to 2021 (2.1 to 7.0%, p &lt; 0.001 for trend), however black individual remained largely underrepresented in all these studies. Conclusions: While the number of black participating in randomized controlled trials involving MM patients over the past five years is significantly increasing, these studies included a disproportionate number of white participants, despite the incidence of MM being higher for black patients. Efforts are underway in the field to enhance enrollment of underrepresented populations, including a recent randomized study that included 19% black MM patients (Richardson, et al. New Eng J Med 2022). Our study was limited due to many trials not reporting details about racial demographics until relatively recently. Further investigations required to examine the reasons underlying racial disparities in MM trial recruitment and enrollment.